Stereo matching is the problem of matching corresponding pixels or other image elements in a stereo image pair. A stereo image pair comprises a left image and a right image of the same scene but where those images are obtained from different viewing locations (such as the left eye and right eye in the case of human vision). If the left and right images are superimposed, the distance between corresponding pixels (pixels that correspond to the same location in the scene being viewed) in the left image and right image is referred to as a disparity. This disparity value provides information about a relative distance of the viewer from the point in the scene being viewed corresponding to the particular pixels being matched. In the case of dense stereo, where all pixels in the image pair are matched the task of matching all those pixels is computationally expensive because the number of possible combinations of pixels is huge. In the case of sparse stereo, where larger image elements such as lines, edges and higher order features are matched the number of possible combinations reduces but the computation is still relatively expensive. The present invention is particularly related to the field of dense stereo although it is potentially also applicable for sparse stereo applications.
In human vision, stereo vision operates over a particular disparity range referred to as Panum's fusional band. Points inside this band are fused visually and the remainder of points are seen as “diplopic”, that is, with double vision. Panum's fusional band may have a range of disparities as little as 1/20th of the full range of disparities for visible points.
The present invention is concerned with ways in which to restrict computation of stereo matching to a volume of interest with a limited range of depth or equivalent disparity.
The invention seeks to provide a method of segmenting image elements into a foreground and background, such that only the foreground elements are part of a volume of interest for stereo matching.